Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
139 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
46 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

A Systematic Review on Custom Data Gloves (2405.15417v1)

Published 24 May 2024 in cs.HC and cs.RO

Abstract: Hands are a fundamental tool humans use to interact with the environment and objects. Through hand motions, we can obtain information about the shape and materials of the surfaces we touch, modify our surroundings by interacting with objects, manipulate objects and tools, or communicate with other people by leveraging the power of gestures. For these reasons, sensorized gloves, which can collect information about hand motions and interactions, have been of interest since the 1980s in various fields, such as Human-Machine Interaction (HMI) and the analysis and control of human motions. Over the last 40 years, research in this field explored different technological approaches and contributed to the popularity of wearable custom and commercial products targeting hand sensorization. Despite a positive research trend, these instruments are not widespread yet outside research environments and devices aimed at research are often ad hoc solutions with a low chance of being reused. This paper aims to provide a systematic literature review for custom gloves to analyze their main characteristics and critical issues, from the type and number of sensors to the limitations due to device encumbrance. The collection of this information lays the foundation for a standardization process necessary for future breakthroughs in this research field.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (246)
  1. D. Sim, Y. Baek, M. Cho, S. Park, A. Sagar, and H. S. Kim, “Low-latency haptic open glove for immersive virtual reality interaction,” Sensors, vol. 21, no. 11, p. 3682, 2021.
  2. Y.-T. Tsai, W.-Y. Jhu, C.-C. Chen, C.-H. Kao, and C.-Y. Chen, “Unity game engine: Interactive software design using digital glove for virtual reality baseball pitch training,” Microsystem Technologies, vol. 27, no. 4, pp. 1401–1417, 2021.
  3. A. Carfì and F. Mastrogiovanni, “Gesture-based human-machine interaction: Taxonomy, problem definition, and analysis,” IEEE Transactions on Cybernetics, vol. 53, pp. 497–513, 2021.
  4. R. Y. Wang and J. Popović, “Real-time hand-tracking with a color glove,” ACM transactions on graphics (TOG), vol. 28, no. 3, pp. 1–8, 2009.
  5. S. Han, B. Liu, R. Wang, Y. Ye, C. D. Twigg, and K. Kin, “Online optical marker-based hand tracking with deep labels,” ACM Transactions on Graphics (TOG), vol. 37, no. 4, pp. 1–10, 2018.
  6. Y. Dong, J. Liu, and W. Yan, “Dynamic hand gesture recognition based on signals from specialized data glove and deep learning algorithms,” IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1–14, 2021.
  7. K. Aw, J. Budd, and T. Wilshaw-Sparkes, “Data glove using soft and stretchable piezoresistive sensors,” Micromachines, vol. 13, no. 3, p. 372, 2022.
  8. L. Seminara, S. Dosen, F. Mastrogiovanni, M. Bianchi, S. Watt, P. Beckerle, T. Nanayakkara, K. Drewing, A. Moscatelli, R. Klatzky, and G. Loeb, “A hierarchical sensorimotor control framework for human-in-the-loop robotic hands,” Science Robotics, vol. 8, no. 78, p. eadd5434, 2023.
  9. J. Wang, B. Li, Z. Li, I. Zubrycki, and G. Granosik, “Grasping behavior of the human hand during tomato picking,” Computers and Electronics in Agriculture, vol. 180, p. 105901, 2021.
  10. S. M. Biju and H. Z. Sheikh, “Sensor evaluation for hand grip strength.” International Journal of Electrical & Computer Engineering (2088-8708), vol. 12, no. 5, 2022.
  11. Y. Liao, Y. Cheng, Z. Zhuang, R. Li, Y. Yu, R. Wang, and Z. Jiao, “Plasma-sprayed flexible strain sensor and its applications in boxing glove,” Applied Sciences, vol. 12, no. 16, p. 8382, 2022.
  12. B. Steffen, G. Christian, B. Rainer, N. Nina, M. Wolfram, and S. Katrin, “Wearable pressure sensing for vojta therapy guidance,” Current Directions in Biomedical Engineering, vol. 6, no. 3, pp. 87–90, 2020.
  13. S. Kerner, M. Krugh, and L. Mears, “Wearable shear and normal force sensing glove development for real-time feedback on assembly line processes,” Journal of Manufacturing Systems, vol. 64, pp. 668–675, 2022.
  14. W. Zheng, N. Guo, B. Zhang, J. Zhou, G. Tian, and Y. Xiong, “Human grasp mechanism understanding, human-inspired grasp control and robotic grasping planning for agricultural robots,” Sensors, vol. 22, no. 14, p. 5240, 2022.
  15. A. T. Maereg, A. Nagar, D. Reid, and E. L. Secco, “Wearable vibrotactile haptic device for stiffness discrimination during virtual interactions,” Frontiers in Robotics and AI, vol. 4, p. 42, 2017.
  16. M. W. Uddin, X. Zhang, and D. Wang, “A pneumatic-driven haptic glove with force and tactile feedback,” in 2016 International Conference on Virtual Reality and Visualization (ICVRV), 2016, pp. 304–311.
  17. P. Ben-Tzvi and Z. Ma, “Sensing and force-feedback exoskeleton (safe) robotic glove,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 23, no. 6, pp. 992–1002, 2015.
  18. T. G. Zimmerman, VPL Research Inc, “Optical flex sensor,” U.S. Patent 4,542,291, 17 September 1985.
  19. T. G. Zimmerman, J. Z. Lanier, VPL Research Inc, “Computer data entry and manipulation apparatus and method,” U.S. Patent 4,988,981, 29 January 1991.
  20. M. Caeiro Rodriguez, I. González, F. Mikic Fonte, and M. Llamas Nistal, “A systematic review of commercial smart gloves: Current status and applications,” Sensors, vol. 21, p. 2667, 04 2021.
  21. L. Dipietro, A. M. Sabatini, and P. Dario, “A survey of glove-based systems and their applications,” IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 38, no. 4, pp. 461–482, 2008.
  22. M. J. Page, J. E. McKenzie, P. M. Bossuyt, I. Boutron, T. C. Hoffmann, C. D. Mulrow, L. Shamseer, J. M. Tetzlaff, E. A. Akl, S. E. Brennan et al., “The prisma 2020 statement: an updated guideline for reporting systematic reviews,” Bmj, vol. 372, 2021.
  23. J. Perret and E. Vander Poorten, “Touching virtual reality: a review of haptic gloves,” in Proceedings of the 16th International Conference on New Actuators, Bremen, Germany, June 2018.
  24. J. Henderson, J. Condell, J. Connolly, D. Kelly, and K. Curran, “Review of wearable sensor-based health monitoring glove devices for rheumatoid arthritis,” Sensors, vol. 21, no. 5, p. 1576, 2021.
  25. R. Li, H. Wang, and Z. Liu, “Survey on mapping human hand motion to robotic hands for teleoperation,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 5, pp. 2647–2665, 2022.
  26. Y. Xue, Z. Ju, K. Xiang, J. Chen, and H. Liu, “Multimodal human hand motion sensing and analysis—a review,” IEEE Transactions on Cognitive and Developmental Systems, vol. 11, no. 2, pp. 162–175, 2018.
  27. W. Chen, C. Yu, C. Tu, Z. Lyu, J. Tang, S. Ou, Y. Fu, and Z. Xue, “A survey on hand pose estimation with wearable sensors and computer-vision-based methods,” Sensors, vol. 20, no. 4, p. 1074, 2020.
  28. S. Crook and P. Chappell, “A portable system for closed loop control of the paralysed hand using functional electrical stimulation,” Medical Engineering & Physics, vol. 20, no. 1, pp. 70–76, 1998.
  29. J. Ku, R. Mraz, N. Baker, K. K. Zakzanis, J.-H. Lee, I.-Y. Kim, S. I. Kim, and S. J. Graham, “A data glove with tactile feedback for fmri of virtual reality experiments,” Cyberpsychology & behavior : the impact of the Internet, multimedia and virtual reality on behavior and society, vol. 6 5, pp. 497–508, 2003.
  30. E. Tanaka, S. Saegusa, Y. Iwasaki, and L. Yuge, “Development of an adl assistance apparatus for upper limbs and evaluation of muscle and cerebral activity of the user,” Journal of Advanced Mechanical Design Systems and Manufacturing, vol. 8, 2013.
  31. F. Lorussi, N. Carbonaro, D. De Rossi, R. Paradiso, P. Veltink, and A. Tognetti, “Wearable textile platform for assessing stroke patient treatment in daily life conditions,” Frontiers in bioengineering and biotechnology, vol. 4, p. 28, 2016.
  32. N. E. H. Saleh, A. Hage-Diab, G. Salhab, B. Debs, M. Hajj-Hassan, H. Khachfe, A. R. Sarraj, and S. Saleh, “Monitoring the use of impaired hand by a new low cost device during daily life activities with real-time visual feedback,” International Journal on Advances in Life Sciences, vol. 8, no. 1&2, 2016.
  33. B.-S. Lin, P.-C. Hsiao, S.-Y. Yang, C.-S. Su, and I.-J. Lee, “Data glove system embedded with inertial measurement units for hand function evaluation in stroke patients,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 25, no. 11, pp. 2204–2213, 2017.
  34. A. Asadipour, K. Debattista, and A. Chalmers, “Visuohaptic augmented feedback for enhancing motor skills acquisition,” The Visual Computer, vol. 33, no. 4, pp. 401–411, 2017.
  35. J. Connolly, J. Condell, B. O’Flynn, J. T. Sanchez, and P. Gardiner, “Imu sensor-based electronic goniometric glove for clinical finger movement analysis,” IEEE Sensors Journal, vol. 18, no. 3, pp. 1273–1281, 2018.
  36. H. Elwahsh, A. Elkhouly, E. A. Nasr, A. K. Kamrani, and E. El-shafeiy, “A new intelligent approach for deaf/dumb people based on deep learning,” CMC-COMPUTERS MATERIALS & CONTINUA, vol. 72, no. 3, pp. 6045–6060, 2022.
  37. V. Stornelli, A. Leoni, G. Ferri, V. Errico, A. Pallotti, G. Orengo, and G. Saggio, “A 10-17 dof sensory gloves with harvesting capability for smart healthcare,” Journal of Communications Software and Systems, vol. 15, no. 2, pp. 166–172, 2019.
  38. M. Hoda, Y. Hoda, B. Hafidh, and A. El Saddik, “Predicting muscle forces measurements from kinematics data using kinect in stroke rehabilitation,” Multimedia Tools and Applications, vol. 77, no. 2, pp. 1885–1903, 2018.
  39. K. Guo, S. Zhang, S. Zhao, and H. Yang, “Design and manufacture of data gloves for rehabilitation training and gesture recognition based on flexible sensors,” Journal of Healthcare Engineering, vol. 2021, 2021.
  40. S. Davarzani, M. A. Ahmadi-Pajouh, and H. Ghafarirad, “Design of sensing system for experimental modeling of soft actuator applied for finger rehabilitation,” Robotica, pp. 1–21, 2021.
  41. S. Elksass, H. A. Alkabes, N. M. El-Kemary, K. E. El-Kelany, and M. El-Kemary, “Anti-bacterial and multi-functional smart wearable sensor based on organo-hydrogel for diagnosis of the anterior cruciate ligament injuries, and sensing glove for rehabilitation of joints motion,” Materials Today Communications, vol. 32, p. 104131, 2022.
  42. H. Sarwat, H. Sarwat, S. A. Maged, T. H. Emara, A. M. Elbokl, and M. I. Awad, “Design of a data glove for assessment of hand performance using supervised machine learning,” Sensors, vol. 21, no. 21, p. 6948, 2021.
  43. A. Maddahi, T. R. Leach, M. Saeedi, P. R. Dhannapuneni, Y. Maddahi, M.-A. Choukou, and K. Zareinia, “Roboethics in remote human interactions and rehabilitative therapeutics,” Applied Sciences, vol. 12, no. 12, p. 6033, 2022.
  44. D. Dutta, S. Aruchamy, S. Mandal, and S. Sen, “Poststroke grasp ability assessment using an intelligent data glove based on action research arm test: Development, algorithms, and experiments,” IEEE Transactions on Biomedical Engineering, vol. 69, no. 2, pp. 945–954, 2022.
  45. F. Amirouche, J. R. Martin, M. Gonzalez, and L. Fergusson, “Experimental set-up and sensory glove interface for microsurgery,” Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, vol. 222, no. 1, pp. 89–99, 2008.
  46. R. C. King, L. Atallah, B. P. L. Lo, and G.-Z. Yang, “Development of a wireless sensor glove for surgical skills assessment,” IEEE Transactions on Information Technology in Biomedicine, vol. 13, no. 5, pp. 673–679, 2009.
  47. P. Falco, G. De Maria, C. Natale, and S. Pirozzi, “Data fusion based on optical technology for observation of human manipulation,” International Journal of Optomechatronics, vol. 6, no. 1, pp. 37–70, 2012.
  48. E. Fujiwara, Y. T. Wu, D. Y. Miyatake, M. F. Santos, and C. K. Suzuki, “Evaluation of thumb-operated directional pad functionalities on a glove-based optical fiber sensor,” IEEE Transactions on Instrumentation and Measurement, vol. 62, no. 8, pp. 2330–2337, 2013.
  49. M. Hazman, I. N. A. Mohd Nordin, F. Hanim, N. Khamis, M. Razif, A. A. Mohd Faudzi, and A. Mohd Hanif, “Imu sensor-based data glove for finger joint measurement,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 20, no. 1, pp. 82–88, 2020.
  50. E. Ayodele, T. Bao, S. A. R. Zaidi, A. M. Hayajneh, J. Scott, Z.-Q. Zhang, and D. McLernon, “Grasp classification with weft knit data glove using a convolutional neural network,” IEEE Sensors Journal, vol. 21, no. 9, pp. 10 824–10 833, 2021.
  51. E. Ayodele, S. Zaidi, J. Scott, Z. Zhang, A. Hayajneh, S. Shittu, and D. McLernon, “A weft knit data glove,” IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1–12, 2021.
  52. J. Maitre, C. Rendu, K. Bouchard, B. Bouchard, and S. Gaboury, “Object recognition in performed basic daily activities with a handcrafted data glove prototype,” Pattern Recognition Letters, vol. 147, pp. 181–188, 2021.
  53. A. Melnyk and P. Hénaff, “Physical analysis of handshaking between humans: mutual synchronisation and social context,” International Journal of Social Robotics, vol. 11, no. 4, pp. 541–554, 2019.
  54. S. Shahrampour, M. Noshad, J. Ding, and V. Tarokh, “Online learning for multimodal data fusion with application to object recognition,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 65, no. 9, pp. 1259–1263, 2017.
  55. C.-H. Kao, C.-C. Chen, W.-Y. Jhu, Y.-T. Tsai, S.-H. Chen, C.-M. Hsu, and C.-Y. Chen, “Novel digital glove design for virtual reality applications,” Microsystem Technologies, vol. 24, no. 10, pp. 4247–4266, 2018.
  56. F. Malawski and J. Gałka, “System for multimodal data acquisition for human action recognition,” Multimedia Tools and Applications, vol. 77, no. 18, pp. 23 825–23 850, 2018.
  57. S. Dalley, A. Varol, and M. Goldfarb, “A method for the control of multigrasp myoelectric prosthetic hands,” IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, vol. 20, no. 1, pp. 58–67, 2011.
  58. P. Girovskỳ and M. Kundrát, “Robotic arm based dynamixel actuators controlled by the data glove,” International Journal of Engineering Research in Africa, vol. 18, p. 152–158, 2015.
  59. M. Yahya, N. Hamzah, A. Othman, A. Diyana, R. Baharudin, and A. I. Che Ani, “Design and development of a mirror effect control prosthetic hand with force sensing,” Telkomnika (Telecommunication Computing Electronics and Control), vol. 15, no. 2, pp. 949–956, 2017.
  60. S. Ullah, Z. Mumtaz, S. Liu, M. Abubaqr, A. Mahboob, and H. A. Madni, “Single-equipment with multiple-application for an automated robot-car control system,” Sensors, vol. 19, no. 3, p. 662, 2019.
  61. S. Li, R. Rameshwar, A. M. Votta, and C. D. Onal, “Intuitive control of a robotic arm and hand system with pneumatic haptic feedback,” IEEE Robotics and Automation Letters, vol. 4, no. 4, pp. 4424–4430, 2019.
  62. S. A. A. Syed Mubarak Ali, N. S. Ahmad, and P. Goh, “Flex sensor compensator via hammerstein–wiener modeling approach for improved dynamic goniometry and constrained control of a bionic hand,” Sensors, vol. 19, no. 18, p. 3896, 2019.
  63. J. D. Setiawan, M. Ariyanto, M. Munadi, M. Mutoha, A. Glowacz, and W. Caesarendra, “Grasp posture control of wearable extra robotic fingers with flex sensors based on neural network,” Electronics, vol. 9, no. 6, p. 905, 2020.
  64. H. Huang, Z. Liang, F. Sun, M. Dong et al., “Virtual interaction and manipulation control of a hexacopter through hand gesture recognition from a data glove,” Robotica, vol. 40, no. 12, pp. 4375–4387, 2022.
  65. B. Fang, F. Sun, H. Liu, and D. Guo, “A novel data glove using inertial and magnetic sensors for motion capture and robotic arm-hand teleoperation,” Industrial Robot: An International Journal, vol. 44, pp. 155–165, 2017.
  66. A. Yudhana, I. C. Kurniawan, I. Anshori, and I. Mufandi, “Performance evaluation of communication methods on electric wheelchairs as assistive technology for persons with disabilities,” International Journal on Smart Sensing and Intelligent Systems, vol. 15, no. 1, 2022.
  67. T. Muezzinoglu and M. Karakose, “An intelligent human–unmanned aerial vehicle interaction approach in real time based on machine learning using wearable gloves,” Sensors, vol. 21, no. 5, p. 1766, 2021.
  68. H. Kim and Y. Choi, “Performance comparison of user interface devices for controlling mining software in virtual reality environments,” Applied Sciences, vol. 9, no. 13, p. 2584, 2019.
  69. Y. Jiang, V. Reimer, T. Schossig, M. Angelmahr, and W. Schade, “Fiber optical multifunctional human-machine interface for motion capture, temperature, and contact force monitoring,” Optics and Lasers in Engineering, vol. 128, p. 106018, 2020.
  70. J. S. Noh, S. K. Lee, and Y. Y. Kim, “Development of hand motion tracking device for agricultural machinery simulator,” Journal of Biosystems Engineering, vol. 45, no. 4, pp. 401–408, 2020.
  71. R. Mraz, J. Hong, G. Quintin, W. R. Staines, W. E. McIlroy, K. K. Zakzanis, and S. J. Graham, “A platform for combining virtual reality experiments with functional magnetic resonance imaging,” CyberPsychology & Behavior, vol. 6, no. 4, pp. 359–368, 2003.
  72. Y. Lin, J. Breugelmans, M. Iversen, and D. Schmidt, “An adaptive interface design (aid) for enhanced computer accessibility and rehabilitation,” International Journal of Human-Computer Studies, vol. 98, 2016.
  73. W. Lin, C. Li, and Y. Zhang, “Interactive application of data glove based on emotion recognition and judgment system,” Sensors, vol. 22, no. 17, p. 6327, 2022.
  74. Y. Wang and J. Sun, “Design and implementation of virtual reality interactive product software based on artificial intelligence deep learning algorithm,” Advances in Multimedia, vol. 2022, 2022.
  75. P. Zhang, W. Li, Q. Zhang, X. Wang, G. Lin, W. Li, Y. Li, K. Zhang, and L. Huang, “Mass-produced flexible strain sensors based on dip-coating and water bath for human–computer interaction,” IEEE Sensors Journal, vol. 23, no. 2, pp. 1497–1506, 2023.
  76. Q. Fu, J. Fu, S. Zhang, X. Li, J. Guo, and S. Guo, “Design of intelligent human-computer interaction system for hard of hearing and non-disabled people,” IEEE Sensors Journal, vol. 21, no. 20, pp. 23 471–23 479, 2021.
  77. N. Tongrod, S. Lokavee, N. Watthanawisuth, and A. Tuantranont, “Design and development of data glove based on printed polymeric sensors and zigbee networks for human–computer interface,” Disability and rehabilitation. Assistive technology, vol. 8, no. 2, pp. 115–20, 2013.
  78. X. Zhang, Z. Yang, T. Chen, D. Chen, and M.-C. Huang, “Cooperative sensing and wearable computing for sequential hand gesture recognition,” IEEE Sensors Journal, vol. 19, no. 14, pp. 5775–5783, 2019.
  79. X. Huang, Q. Wang, S. Zang, J. Wan, G. Yang, Y. Huang, and X. Ren, “Tracing the motion of finger joints for gesture recognition via sewing rgo-coated fibers onto a textile glove,” IEEE sensors journal, vol. 19, no. 20, pp. 9504–9511, 2019.
  80. V. Mehra, A. Choudhury, and R. R. Choubey, “Gesture to speech conversion using flex sensors mpu6050 and python,” Published in International Journal of Engineering and Advanced Technology (IJEAT), vol. 8, no. 6, pp. 4686–4690, 2019.
  81. W.-C. Chuang, W.-J. Hwang, T.-M. Tai, D.-R. Huang, and Y.-J. Jhang, “Continuous finger gesture recognition based on flex sensors,” Sensors, vol. 19, no. 18, p. 3986, 2019.
  82. H.-T. Chang and J.-Y. Chang, “Sensor glove based on novel inertial sensor fusion control algorithm for 3-d real-time hand gestures measurements,” IEEE Transactions on Industrial Electronics, vol. 67, no. 1, pp. 658–666, 2020.
  83. P. Chitra, S. Manickam, R. Prabha, A. Sahaya, A. Nisha, and T. Bernatin, “Gloves gesture recognition,” International Journal of Scientific & Technology Research, 05 2022.
  84. J. Pan, Y. Luo, Y. Li, C.-K. Tham, C.-H. Heng, and A. V.-Y. Thean, “A wireless multi-channel capacitive sensor system for efficient glove-based gesture recognition with ai at the edge,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 67, no. 9, pp. 1624–1628, 2020.
  85. P. Jhamb and A. Rehalia, “Wireless hand gesture controlled multiple musical instruments,” International Journal of Information Technology, vol. 12, no. 1, pp. 19–25, 2020.
  86. S. Shin, H. U. Yoon, and B. Yoo, “Hand gesture recognition using egain-silicone soft sensors,” Sensors, vol. 21, no. 9, p. 3204, 2021.
  87. G. Yuan, X. Liu, Q. Yan, S. Qiao, Z. Wang, and L. Yuan, “Hand gesture recognition using deep feature fusion network based on wearable sensors,” IEEE Sensors Journal, vol. 21, no. 1, pp. 539–547, 2020.
  88. L. Yuan, W. Qi, K. Cai, C. Li, Q. Qian, and Y. Zhou, “Gesture recognition device based on cross reticulated graphene strain sensors,” Journal of Materials Science: Materials in Electronics, vol. 32, no. 7, pp. 8410–8417, 2021.
  89. M. Lee and J. Bae, “Real-time gesture recognition in the view of repeating characteristics of sign languages,” IEEE Transactions on Industrial Informatics, vol. 18, no. 12, pp. 8818–8828, 2022.
  90. J. Li, X. Liu, Z. Wang, T. Zhang, S. Qiu, H. Zhao, X. Zhou, H. Cai, R. Ni, and A. Cangelosi, “Real-time hand gesture tracking for human–computer interface based on multi-sensor data fusion,” IEEE Sensors Journal, vol. 21, no. 23, pp. 26 642–26 654, 2021.
  91. M. Ahmed, B. Zaidan, A. Zaidan, A. Alamoodi, O. Albahri, Z. Al-Qaysi, A. Albahri, and M. M. Salih, “Real-time sign language framework based on wearable device: analysis of msl, dataglove, and gesture recognition,” Soft Computing, vol. 25, no. 16, pp. 11 101–11 122, 2021.
  92. J. Piskozub and P. Strumillo, “Reducing the number of sensors in the data glove for recognition of static hand gestures,” Applied Sciences, vol. 12, no. 15, p. 7388, 2022.
  93. A. Calado, P. Roselli, V. Errico, N. Magrofuoco, J. Vanderdonckt, and G. Saggio, “A geometric model-based approach to hand gesture recognition,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 10, pp. 6151–6161, 2022.
  94. A. Hekmat, Z. Zuping, and H. S. S. Al-deen, “Map modeling for full body gesture using flex sensor and machine learning algorithms,” Multimedia Systems, pp. 1–16, 2022.
  95. D. W. O. Antillon, C. R. Walker, S. Rosset, and I. A. Anderson, “Glove-based hand gesture recognition for diver communication,” IEEE Transactions on Neural Networks and Learning Systems, pp. 1–13, 2022.
  96. T. D. Bui and L. Thang Nguyen, “Recognizing postures in vietnamese sign language with mems accelerometers,” IEEE Sensors Journal, vol. 7, no. 5, pp. 707–712, 2007.
  97. A. Z. Shukor, M. F. Miskon, M. H. Jamaluddin, F. bin Ali, M. F. Asyraf, M. B. bin Bahar et al., “A new data glove approach for malaysian sign language detection,” Procedia Computer Science, vol. 76, pp. 60–67, 2015.
  98. K.-W. Kim, M.-S. Lee, B.-R. Soon, M.-H. Ryu, and J.-N. Kim, “Recognition of sign language with an inertial sensor-based data glove,” Technology and Health Care, vol. 24, no. s1, pp. S223–S230, 2016.
  99. J. Galka, M. Masior, M. Zaborski, and K. Barczewska, “Inertial motion sensing glove for sign language gesture acquisition and recognition,” IEEE Sensors Journal, vol. 16, no. 16, pp. 6310–6316, 2016.
  100. K. Li, Z. Zhou, and C.-H. Lee, “Sign transition modeling and a scalable solution to continuous sign language recognition for real-world applications,” ACM Transactions on Accessible Computing (TACCESS), vol. 8, no. 2, pp. 1–23, 2016.
  101. G. Saldaña González, J. Cerezo Sánchez, M. M. Bustillo Díaz, and A. Ata Pérez, “Recognition and classification of sign language for spanish,” Computación y Sistemas, vol. 22, no. 1, pp. 271–277, 2018.
  102. G. Puasa, “Data glove for american sign language alphabet and numbers (1-9) translation system,” International Journal of Advanced Trends in Computer Science and Engineering, vol. 8, no. 4, pp. 1128–1133, 2019.
  103. V. Shweta, D. A. Asarpota, H. Verma et al., “User trainable sign language to speech glove using knn classifier,” Compusoft, vol. 3053, no. 3058, p. 8, 2019.
  104. A. H. Alrubayi, M. Ahmed, A. Zaidan, A. Albahri, B. Zaidan, O. Albahri, A. Alamoodi, and M. Alazab, “A pattern recognition model for static gestures in malaysian sign language based on machine learning techniques,” Computers & Electrical Engineering, vol. 95, p. 107383, 2021.
  105. J. DelPreto, J. Hughes, M. D’Aria, M. de Fazio, and D. Rus, “A wearable smart glove and its application of pose and gesture detection to sign language classification,” IEEE Robotics and Automation Letters, vol. 7, no. 4, pp. 10 589–10 596, 2022.
  106. T. S. Dias, J. J. A. M. Junior, and S. F. Pichorim, “Comparison between handcraft feature extraction and methods based on recurrent neural network models for gesture recognition by instrumented gloves: A case for brazilian sign language alphabet,” Biomedical Signal Processing and Control, vol. 80, p. 104201, 2023.
  107. A. Calado, V. Errico, and G. Saggio, “Toward the minimum number of wearables to recognize signer-independent italian sign language with machine-learning algorithms,” IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1–9, 2021.
  108. Y. Zhang, W. Xu, X. Zhang, and L. Li, “Sign annotation generation to alphabets via integrating visual data with somatosensory data from flexible strain sensor-based data glove,” Measurement, vol. 202, p. 111700, 2022.
  109. T. Simoes Dias, J. J. A. M. Junior, and S. F. Pichorim, “An instrumented glove for recognition of brazilian sign language alphabet,” IEEE Sensors Journal, vol. 22, no. 3, pp. 2518–2529, 2022.
  110. M. A. Ahmed, B. Bahaa, A. Zaidan, M. Salih, Z. Al-qaysi, and A. Alamoodi, “Based on wearable sensory device in 3d-printed humanoid: A new real-time sign language recognition system,” Measurement, vol. 168, p. 108431, 2021.
  111. Y. Su, M. Fisher, A. Wolczowski, G. Bell, D. Burn, and R. Gao, “Towards an emg controlled prosthetic hand using a 3d electromagnetic positioning system,” in IEEE Instrumentation and Measurement Technology Conference Proceedings, Ottawa, ON, Canada, May 2005.
  112. C.-S. Fahn and H. Sun, “Development of a fingertip glove equipped with magnetic tracking sensors,” Sensors (Basel, Switzerland), vol. 10, no. 2, pp. 1119–40, 2010.
  113. A. C. Yuen, A. A. Bakir, N. N. Z. M. Rajdi, C. L. Lam, S. M. Saleh, and D. H. Wicaksono, “Proprioceptive sensing system for therapy assessment using cotton fabric-based biomedical microelectromechanical system,” IEEE Sensors Journal, vol. 14, no. 8, pp. 2872–2880, 2014.
  114. A. S. Zuruzi, T. M. Haffiz, D. Affidah, A. Amirul, A. Norfatriah, and M. H. Nurmawati, “Towards wearable pressure sensors using multiwall carbon nanotube/polydimethylsiloxane nanocomposite foams,” Materials & Design, vol. 132, pp. 449–458, 2017.
  115. Q. Zhang, Y. L. Wang, Y. Xia, P. F. Zhang, T. V. Kirk, and X. D. Chen, “Textile-only capacitive sensors for facile fabric integration without compromise of wearability,” Advanced Materials Technologies, vol. 4, no. 10, p. 1900485, 2019.
  116. V. Minh, N. Katushin, and J. Pumwa, “Motion tracking glove for augmented reality and virtual reality,” Paladyn, Journal of Behavioral Robotics, vol. 10, no. 1, pp. 160–166, 2019.
  117. J. Song, S. Chen, L. Sun, Y. Guo, L. Zhang, S. Wang, H. Xuan, Q. Guan, and Z. You, “Mechanically and electronically robust transparent organohydrogel fibers,” Advanced Materials, vol. 32, no. 8, p. 1906994, 2020.
  118. Y. Zhang, Y. Huang, P. Liu, C. Liu, X. Guo, and Y. Zhang, “Highly stretchable strain sensor with wide linear region via hydrogen bond-assisted dual-mode cooperative conductive network for gait detection,” Composites Science and Technology, vol. 191, p. 108070, 2020.
  119. Y. Li, H. Di, Y. Xin, and X. Jiang, “Optical fiber data glove for hand posture capture,” Optik, vol. 233, p. 166603, 02 2021.
  120. G. Nantzios, N. Baras, and M. Dasygenis, “Design and implementation of a robotic arm assistant with voice interaction using machine vision,” Automation, vol. 2, no. 4, pp. 238–251, 2021.
  121. C. Wu, K. Wang, Q. Cao, F. Fei, D. Yang, X. Lu, B. Xu, H. Zeng, and A. Song, “Development of a low-cost wearable data glove for capturing finger joint angles,” Micromachines, vol. 12, no. 7, p. 771, 2021.
  122. J. Nassour, H. G. Amirabadi, S. Weheabby, A. Al Ali, H. Lang, and F. Hamker, “A robust data-driven soft sensory glove for human hand motions identification and replication,” IEEE Sensors Journal, vol. 20, no. 21, pp. 12 972–12 979, 2020.
  123. Y. Zhang, Y. Huang, X. Sun, Y. Zhao, X. Guo, P. Liu, C. Liu, and Y. Zhang, “Static and dynamic human arm/hand gesture capturing and recognition via multiinformation fusion of flexible strain sensors,” IEEE Sensors Journal, vol. 20, no. 12, pp. 6450–6459, 2020.
  124. Q. Liu, G. Qian, W. Meng, Q. Ai, C. Yin, and Z. Fang, “A new immu-based data glove for hand motion capture with optimized sensor layout,” International Journal of Intelligent Robotics and Applications, vol. 3, no. 1, pp. 19–32, 2019.
  125. W. Zhang, J. Z. Yu, F. Zhu, Y. Zhu, Z. Yang, N. G. Ulu, B. Arisoy, and L. B. Kara, “High degree of freedom hand pose tracking using limited strain sensing and optical training,” Journal of Computing and Information Science in Engineering, vol. 19, no. 3, 2019.
  126. Y. F. Chacon, M. Plaza, and A. M. Cifuentes, “Development and design wearable sensorial system of low cost to determine the metacarpophalangeal and interphalangeal joints angles,” ECS Transactions, vol. 86, no. 16, p. 39, 2018.
  127. A. F. Da Silva, A. F. Gonçalves, P. M. Mendes, and J. H. Correia, “Fbg sensing glove for monitoring hand posture,” IEEE Sensors Journal, vol. 11, no. 10, pp. 2442–2448, 2011.
  128. E. Cazacu, C. van der Grinten, J. Bax, G. Baeten, F. Holtkamp, and C. Lee, “A position sensing glove to aid ankle-foot orthosis diagnosis and treatment,” Sensors, vol. 21, no. 19, p. 6631, 2021.
  129. X. Liao, W. Wang, M. Lin, M. Li, H. Wu, and Y. Zheng, “Hierarchically distributed microstructure design of haptic sensors for personalized fingertip mechanosensational manipulation,” Materials Horizons, vol. 5, no. 5, pp. 920–931, 2018.
  130. A. Carfì, T. Patten, Y. Kuang, A. Hammoud, M. Alameh, E. Maiettini, A. I. Weinberg, D. Faria, F. Mastrogiovanni, G. Alenyà, L. Natale, V. Perdereau, M. Vincze, and A. Billard, “Hand-object interaction: From human demonstrations to robot manipulation,” Frontiers in Robotics and AI, vol. 8, p. 714023, 2021.
  131. Z. Johor, R. Candra, W. Rasyid, A. Asnaldi, O. Oktarifaldi, and S. Bakhtiar, “Effect of hand-eye coordination on the capability of children object control,” 01 2020.
  132. R. Johansson and J. Flanagan, “Tactile sensory control of object manipulation in humans,” The Senses: A Comprehensive Reference, vol. 6, pp. 67–86, 01 2010.
  133. V. Mašić, A. Šečić, T. T. Bobić, and L. Femec, “Neuroplasticity and braille reading,” Acta Clinica Croatica, vol. 59, no. 1, p. 147, 2020.
  134. F. Buonamici, R. Furferi, L. Governi, and Y. Volpe, “Making blind people autonomous in the exploration of tactile models: A feasibility study,” 08 2015, pp. 82–93.
  135. S. Rath, “Hand kinematics: application in clinical practice,” Indian journal of plastic surgery, vol. 44, no. 02, pp. 178–185, 2011.
  136. M. Hossain, M. Sharifi, and S. Degadwala, “Design and implementation of humanoid robotic arm,” International Journal of Advanced Research in Science, Communication and Technology, pp. 7–21, 2020.
  137. Y. Luo, X. Chen, X. Li, H. Tian, S. Li, L. Wang, J. He, Z. Yang, and J. Shao, “Heterogeneous strain distribution based programmable gated microchannel for ultrasensitive and stable strain sensing,” Advanced Materials, p. 2207141, 2022.
  138. A. A. Zakri, A. H. Arfianti Arfianti, M. Iqbal, H. Madjid, and N. F. Aulia, “Designing flex sensor gloves with temperature sensor & pulse sensor to help stroke patients,” International Journal of Emerging Technology and Advanced Engineering, vol. 12, pp. 23–31, 2022.
  139. S. Duan, J. Wang, Y. Lin, J. Hong, Y. Lin, Y. Xia, Y. Li, D. Zhu, W. Lei, W. Su et al., “Highly durable machine-learned waterproof electronic glove based on low-cost thermal transfer printing for amphibious wearable applications,” Nano Research, pp. 1–10, 2022.
  140. F. Huang, J. Hu, X. Yan, and F. Meng, “High-linearity, ultralow-detection-limit, and rapid-response strain sensing yarn for data gloves,” Journal of Industrial Textiles, vol. 51, no. 3_suppl, pp. 4554S–4570S, 2022.
  141. X. Han, X. Miao, Q. Liu, Y. Li, and A. Wan, “A fabric-based integrated sensor glove system recognizing hand gesture,” Autex Research Journal, vol. 0, no. 0, 2021.
  142. Thomas G. Zimmerman, “Optical flex sensor,” U.S. Patent 4,542,291, 17 September 1985.
  143. E. Fujiwara, M. Santos, and C. Suzuki, “Flexible optical fiber bending transducer for application in glove-based sensors,” Sensors Journal, IEEE, vol. 14, pp. 3631–3636, 10 2014.
  144. James S. NeelyPhillip J. Restle, “Capacitive bend sensor,” U.S. Patent 5,610,528, 11 March 1997.
  145. Gordon B. Langford, “Flexible potentiometer,” U.S. Patent 5,583,476, 10 December 1996.
  146. S. Lee, Y. Choi, M. Sung, J. Bae, and Y. Choi, “A knitted sensing glove for human hand postures pattern recognition,” Sensors, vol. 21, no. 4, p. 1364, 2021.
  147. Y. Choi, K. Yoo, S. J. Kang, B. Seo, and S. K. Kim, “Development of a low-cost wearable sensing glove with multiple inertial sensors and a light and fast orientation estimation algorithm,” The Journal of Supercomputing, vol. 74, no. 8, pp. 3639–3652, 2018.
  148. P. Gui, L. Tang, and S. Mukhopadhyay, “Mems based imu for tilting measurement: Comparison of complementary and kalman filter based data fusion,” in 2015 IEEE 10th conference on Industrial Electronics and Applications (ICIEA), 2015, pp. 2004–2009.
  149. F. Narváez, F. Árbito, and R. Proaño, “A quaternion-based method to imu-to-body alignment for gait analysis,” in Proceedings of the 9th International Conference of Digital Human Modeling: Applications in Health, Safety, Ergonomics, and Risk Management, Las Vegas, NV, USA, July 2018.
  150. M. Guidolin, R. A. Budau Petrea, O. Roberto, M. Reggiani, E. Menegatti, and L. Tagliapietra, “On the accuracy of imus for human motion tracking: a comparative evaluation,” in 2021 IEEE International Conference on Mechatronics (ICM), 2021, pp. 1–6.
  151. C.-S. Fahn and H. Sun, “Development of a data glove with reducing sensors based on magnetic induction,” IEEE Transactions on Industrial Electronics, vol. 52, no. 2, pp. 585–594, 2005.
  152. F. Santoni, A. De Angelis, A. Moschitta, and P. Carbone, “Magik: A hand-tracking magnetic positioning system based on a kinematic model of the hand,” IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1–13, 2021.
  153. L. Pacher, C. Chatellier, R. Vauzelle, and L. Fradet, “Sensor-to-segment calibration methodologies for lower-body kinematic analysis with inertial sensors: A systematic review,” Sensors, vol. 20, no. 11, p. 3322, 2020.
  154. R. V. Vitali and N. C. Perkins, “Determining anatomical frames via inertial motion capture: A survey of methods,” Journal of Biomechanics, vol. 106, p. 109832, 2020.
  155. L. Pacher, C. Chatellier, R. Vauzelle, and L. Fradet, “Sensor-to-segment calibration methodologies for lower-body kinematic analysis with inertial sensors: A systematic review,” Sensors, vol. 20, no. 11, 2020.
  156. J. Favre, B. Jolles, R. Aissaoui, and K. Aminian, “Ambulatory measurement of 3d knee joint angle,” Journal of biomechanics, vol. 41, no. 5, pp. 1029–1035, 2008.
  157. S. Cordillet, N. Bideau, B. Bideau, and G. Nicolas, “Estimation of 3d knee joint angles during cycling using inertial sensors: Accuracy of a novel sensor-to-segment calibration procedure based on pedaling motion,” Sensors, vol. 19, no. 11, 2019.
  158. T. Seel, T. Schauer, and J. Raisch, “Joint axis and position estimation from inertial measurement data by exploiting kinematic constraints,” in 2012 IEEE International Conference on Control Applications.   IEEE, 2012, pp. 45–49.
  159. P. Picerno, P. Caliandro, C. Iacovelli, C. Simbolotti, M. Crabolu, D. Pani, G. Vannozzi, G. Reale, P. M. Rossini, L. Padua et al., “Upper limb joint kinematics using wearable magnetic and inertial measurement units: an anatomical calibration procedure based on bony landmark identification,” Scientific reports, vol. 9, no. 1, pp. 1–10, 2019.
  160. I. Weygers, M. Kok, T. Seel, D. Shah, O. Taylan, L. Scheys, H. Hallez, and K. Claeys, “In-vitro validation of inertial-sensor-to-bone alignment,” Journal of Biomechanics, vol. 128, p. 110781, 2021.
  161. L. Jiang, H. Xia, and C. Guo, “A model-based system for real-time articulated hand tracking using a simple data glove and a depth camera,” Sensors, vol. 19, no. 21, p. 4680, 2019.
  162. N. Karlsson, B. Karlsson, and P. Wide, “A glove equipped with finger flexion sensors as a command generator used in a fuzzy control system,” IEEE Transactions on Instrumentation and Measurement, vol. 47, no. 5, pp. 1330–1334, 1998.
  163. A. Mazzoldi, D. D. Rossi, F. Lorussi, E. P. Scilingo, and R. Paradiso, “Smart textiles for wearable motion capture systems,” AUTEX Research Journal, vol. 2, no. 4, pp. 199–203, 2002.
  164. K. Tarchanidis and J. Lygouras, “Data glove with a force sensor,” IEEE T. Instrumentation and Measurement, vol. 52, no. 3, pp. 984–989, 2003.
  165. G. Bao, “Force feedback dataglove based on pneumatic artificial muscles,” Chinese Journal of Mechanical Engineering - CHIN J MECH ENG, vol. 19, no. 4, p. 588, 2006.
  166. S. You, T. Wang, R. Eagleson, C. Meng, and Q. Zhang, “A low-cost internet-based telerobotic system for access to remote laboratories,” Artif. Intell. Eng., vol. 15, pp. 265–279, 2001.
  167. H. Zhou, T. Lu, Y. Liu, S. Zhang, and M. Gowda, “Learning on the rings: Self-supervised 3d finger motion tracking using wearable sensors,” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 6, no. 2, pp. 1–31, 2022.
  168. M. Bouzit, G. Burdea, G. Popescu, and R. Boian, “The rutgers master ii-new design force-feedback glove,” IEEE/ASME Transactions on Mechatronics, vol. 7, no. 2, pp. 256–263, 2002.
  169. M. Pesenti, G. Invernizzi, J. Mazzella, M. Bocciolone, A. Pedrocchi, and M. Gandolla, “Imu-based human activity recognition and payload classification for low-back exoskeletons,” Scientific Reports, vol. 13, 01 2023.
  170. S. Zihajehzadeh and E. J. Park, “Experimental evaluation of regression model-based walking speed estimation using lower body-mounted imu,” in 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).   IEEE, 2016, pp. 243–246.
  171. L. Lastrico, V. Belcamino, A. Carfì, A. Vignolo, A. Sciutti, F. Mastrogiovanni, and F. Rea, “The effects of selected object features on a pick-and-place task: A human multimodal dataset,” The International Journal of Robotics Research, p. 02783649231210965, 2024.
  172. D. Lauss, F. Eibensteiner, and P. Petz, “A deep learning based hand gesture recognition on a low-power microcontroller using imu sensors,” in 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA), 2022, pp. 733–736.
  173. M. F. Trujillo-Guerrero, S. Román-Niemes, M. Jaén-Vargas, A. Cadiz, R. Fonseca, and J. J. Serrano-Olmedo, “Accuracy comparison of cnn, lstm, and transformer for activity recognition using imu and visual markers,” IEEE Access, vol. 11, pp. 106 650–106 669, 2023.
  174. M. Králik and M. Šuppa, “Waveglove: Transformer-based hand gesture recognition using multiple inertial sensors,” in 2021 29th European Signal Processing Conference (EUSIPCO), 2021, pp. 1576–1580.
  175. A. Hoelzemann, N. Sorathiya, and K. Van Laerhoven, “Data augmentation strategies for human activity data using generative adversarial neural networks,” in 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), 2021, pp. 8–13.
  176. J. Justa, V. Smidl, and A. Hamáček, “Deep learning methods for speed estimation of bipedal motion from wearable imu sensors,” Sensors (Basel, Switzerland), vol. 22, 05 2022.
  177. Å. Vallbo, K. Olsson, K.-G. Westberg, and F. Clark, “Microstimulation of single tactile afferents from the human hand: Sensory attributes related to unit type and properties of receptive fields,” Brain, vol. 107, no. 3, pp. 727–749, 1984.
  178. R. S. Johansson and J. R. Flanagan, “Coding and use of tactile signals from the fingertips in object manipulation tasks,” Nature Reviews Neuroscience, vol. 10, no. 5, pp. 345–359, 2009.
  179. C.-Z. Hang, X.-F. Zhao, S.-Y. Xi, Y.-H. Shang, K.-P. Yuan, F. Yang, Q.-G. Wang, J.-C. Wang, D. W. Zhang, and H.-L. Lu, “Highly stretchable and self-healing strain sensors for motion detection in wireless human-machine interface,” Nano Energy, vol. 76, p. 105064, 2020.
  180. Y. Zhang, Y. Huang, Y. Ge, and N. Bao, “A master–slave hand operation cooperative perception system for grasping object via information fusion of flexible strain sensors,” Measurement, vol. 169, p. 108437, 2021.
  181. Z. Li, L. Cheng, and Q. Song, “An ultra-stretchable and highly sensitive photoelectric effect-based strain sensor: Implementation and applications,” IEEE Sensors Journal, vol. 21, no. 4, pp. 4365–4376, 2020.
  182. S. M. Biju, H. Z. Sheikh, M. F. Malek, F. Oroumchian, and A. Bell, “Design of grip strength measuring system using FSR and flex sensors using SVM algorithm,” IAES International Journal of Artificial Intelligence (IJ-AI), vol. 10, no. 3, p. 676, 2021.
  183. J.-H. Lee, Y.-S. Lee, S.-H. Park, M.-C. Park, B.-K. Yoo, and S.-M. In, “A study on the human grip force distribution on the cylindrical handle by intelligent force glove(i-force glove),” in 2008 International Conference on Control, Automation and Systems, 2008, pp. 966–969.
  184. F. Leo, G. Sandini, and A. Sciutti, “Mental rotation skill shapes haptic exploration strategies,” IEEE Transactions on Haptics, pp. 1–1, 2022.
  185. N. Wettels, V. Santos, R. Johansson, and G. Loeb, “Biomimetic tactile sensor array,” Advanced Robotics, vol. 22, no. 8, pp. 829–849, 2008.
  186. T. P. Tomo, A. Schmitz, W. K. Wong, H. Kristanto, S. Somlor, J. Hwang, L. Jamone, and S. Sugano, “Covering a robot fingertip with uskin: A soft electronic skin with distributed 3-axis force sensitive elements for robot hands,” IEEE Robotics and Automation Letters, vol. 3, no. 1, pp. 124–131, 2018.
  187. W. Wasko, A. Albini, P. Maiolino, F. Mastrogiovanni, and G. Cannata, “Contact modeling and tactile data processing for robot skin,” Sensors, vol. 19, no. 4, p. 814, 2019.
  188. R. Dahiya, G. Metta, M. Valle, and G. Sandini, “Tactile sensing—from humans to humanoids,” IEEE Transactions on Robotics, vol. 26, no. 1, pp. 1–20, 2010.
  189. A. Schmitz, P. Maiolino, M. Maggiali, L. Natale, G. Cannata, and G. Metta, “Methods and technologies for the implementation of large-scale robot tactile sensors,” IEEE Transactions on Robotics, vol. 27, no. 3, pp. 389–400, 2011.
  190. A. Billard, A. Bonfiglio, G. Cannata, P. Cosseddu, T. Dahl, K. Dautenhahn, F. Mastrogiovanni, G. Metta, L. Natale, B. Robins, L. Seminara, and M. Valle, “The ROBOSKIN project: challenges and results,” in ROMANSY 19 – Robot Design, Dynamics and Control, V. Padois, P. Bidaud, and O. Khatib, Eds.   Vienna, Austria: Springer, 2013.
  191. G. Cheng, E. Dean-Leon, F. Bergner, J. R. G. Olvera, Q. Leboutet, and P. Mittendorfer, “A comprehensive realization of robot skin: sensors, sensing, control, and applications,” Proceedings of the IEEE, vol. 107, no. 10, pp. 2034– –2051, 2019.
  192. H. Kristanto, P. Sathe, A. Schmitz, T. P. Tomo, S. Somlor, and S. Sugano, “A wearable three-axis tactile sensor for human fingertips,” IEEE Robotics and Automation Letters, vol. 3, no. 4, pp. 4313–4320, 2018.
  193. P. Maiolino, M. Maggiali, G. Cannata, G. Metta, and L. Natale, “A flexible and robust large scale capacitive tactile system for robots,” IEEE Sensors Journal, vol. 13, no. 10, pp. 3910–3917, 2013.
  194. E. Baglini, G. Cannata, and F. Mastrogiovanni, “Design of an embedded networking infrastructure for whole-body tactile sensing in humanoid robots,” in Proc. 10th IEEE-RAS International Conference on Humanoid Robots (HUMANOIDS), Bled, Slovenia, December 2010.
  195. S. Youssefi, S. Denei, F. Mastrogiovanni, and G. Cannata, “A real-time data acquisition and processing framework for large-scale robot skin,” Robotics and Autonomous Systems, vol. 68, pp. 86–103, 2015.
  196. A. Loi, L. Basiricò, P. Cosseddu, S. Lai, M. Barbaro, A. Bonfiglio, P. Maiolino, E. Baglini, S. Denei, F. Mastrogiovanni, and G. Cannata, “Organic bendable and stretchable field effect devices for sensing applications,” IEEE Sensors Journal, vol. 13, no. 12, pp. 4764–4772, 2013.
  197. P. Ben-Tzvi and Z. Ma, “Sensing and force-feedback exoskeleton (safe) robotic glove,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 23, no. 6, pp. 992–1002, 2014.
  198. H. J. Luinge, D. Roetenberg, and P. J. Slycke, “Inertial sensor kinematic coupling,” Feb. 3 2011, uS Patent App. 12/534,526.
  199. X. Yun and E. R. Bachmann, “Design, implementation, and experimental results of a quaternion-based kalman filter for human body motion tracking,” IEEE Transactions on Robotics, vol. 22, no. 6, pp. 1216–1227, 2006.
  200. G. Saggio, F. Riillo, L. Sbernini, and L. R. Quitadamo, “Resistive flex sensors: a survey,” Smart Materials and Structures, vol. 25, no. 1, p. 013001, 2015.
  201. Y.-F. Fu and C.-S. Ho, “A user-dependent easily-adjusted static finger language recognition system for handicapped aphasiacs,” Applied Artificial Intelligence, vol. 23, no. 10, pp. 932–944, 2009.
  202. ——, “Building intelligent communication systems for handicapped aphasiacs,” Sensors, vol. 10, no. 1, pp. 374–387, 2010.
  203. P.-J. Chen and Y.-C. Du, “Combining independent component and grey relational analysis for the real-time system of hand motion identification using bend sensors and multichannel surface emg,” Mathematical Problems in Engineering, vol. 2015, pp. 1–9, 2015.
  204. G. K. Saini and R. Kaur, “Designing real-time virtual instrumentation system for differently abled using labview,” International Journal of Biomedical Engineering and Technology, vol. 18, no. 1, pp. 86–101, 2015.
  205. D. H. Kim, S. W. Lee, and H.-S. Park, “Improving kinematic accuracy of soft wearable data gloves by optimizing sensor locations,” Sensors, vol. 16, no. 6, p. 766, 2016.
  206. A. I. Che-Ani, A. Othman, N. Hamzah, A. D. Rosli, R. Baharudin, and M. F. Abdullah, “Real-time finger hand movement capturing via a data hand glove,” ARPN journal of engineering and applied sciences, vol. 11, pp. 4877–4881, 2016.
  207. C. Lu, J. Chen, T. Jiang, G. Gu, W. Tang, and Z. L. Wang, “A stretchable, flexible triboelectric nanogenerator for self-powered real-time motion monitoring,” Advanced Materials Technologies, vol. 3, no. 6, p. 1800021, 2018.
  208. M. Javaid, “Communication through haptics during human collaborative manipulation,” International Journal of Humanoid Robotics, vol. 15, no. 03, p. 1850003, 2018.
  209. R. Z. R. Umar, C. F. Ling, N. Abdullasim, N. Ahmad, I. Halim, and M. Hamid, “Occupational wrist postural assessment and monitoring system: development and initial validation,” Journal of Engineering Science and Technology, vol. 14, no. 6, pp. 3421–3426, 2019.
  210. C. Lim, H. sang Ko, S. Cho, I. Ohu, H. E. Wang, B. Jimmy, J. Felice, R. E. Griffin, and J. N. Carlson, “Development of classification models for assessment of endotracheal intubation training by a cyber-physical system,” Procedia Manufacturing, vol. 39, pp. 357–362, 2019.
  211. W. Dong, L. Yang, and G. Fortino, “Stretchable human machine interface based on smart glove embedded with pdms-cb strain sensors,” IEEE Sensors Journal, vol. 20, no. 14, pp. 8073–8081, 2020.
  212. B.-S. Lin, I.-J. Lee, P.-C. Hsiao, S.-Y. Yang, C.-Y. Chen, S.-H. Lee, Y.-F. Huang, M.-H. Yen, and Y. H. Hu, “Design of a multi-sensor system for exploring the relation between finger spasticity and voluntary movement in patients with stroke,” Sensors, vol. 22, no. 19, p. 7212, 2022.
  213. T. Cerqueira, F. M. Ribeiro, V. H. Pinto, J. Lima, and G. Gonçalves, “Glove prototype for feature extraction applied to learning by demonstration purposes,” Applied Sciences, vol. 12, no. 21, p. 10752, 2022.
  214. T. Bright, S. Adali, and G. Bright, “Low-cost sensory glove for human–robot collaboration in advanced manufacturing systems,” Robotics, vol. 11, no. 3, p. 56, 2022.
  215. Y.-T. Hwang, W.-A. Lu, and B.-S. Lin, “Use of functional data to model the trajectory of an inertial measurement unit and classify levels of motor impairment for stroke patients,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 30, pp. 925–935, 2022.
  216. N. B. Gosala, F. Wang, Z. Cui, H. Liang, O. Glauser, S. Wu, and O. Sorkine-Hornung, “Self-calibrated multi-sensor wearable for hand tracking and modeling,” IEEE Transactions on Visualization and Computer Graphics, pp. 1–1, 2021.
  217. Y. Ganjdanesh, K. Maghooli, A. M. Nasrabadi, and M.-S. Moein, “A new method for signature verification based on physiological characteristics of hand muscles and tendons,” Biomedical Engineering: Applications, Basis and Communications, vol. 29, no. 01, p. 1750006, 2017.
  218. O. Glauser, S. Wu, D. Panozzo, O. Hilliges, and O. Sorkine-Hornung, “Interactive hand pose estimation using a stretch-sensing soft glove,” ACM Transactions on Graphics (TOG), vol. 38, no. 4, pp. 1–15, 2019.
  219. B.-S. Lin, I.-J. Lee, P.-Y. Chiang, S.-Y. Huang, and C.-W. Peng, “A modular data glove system for finger and hand motion capture based on inertial sensors,” Journal of Medical and Biological Engineering, vol. 39, 06 2018.
  220. B.-S. Lin, I.-J. Lee, P.-C. Hsiao, and Y.-T. Hwang, “An assessment system for post-stroke manual dexterity using principal component analysis and logistic regression,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 27, no. 8, pp. 1626–1634, 2019.
  221. K. J. Zwieten, K. Schmidt, G. Bex, P. Lippens, and W. Duyvendak, “An analytical expression for the d.i.p. - p.i.p. flexion interdependence in human fingers,” Acta of bioengineering and biomechanics / Wroclaw University of Technology, vol. 17, pp. 129 – 135, 04 2015.
  222. Y. Su, C. Allen, D. Geng, D. Burn, U. Brechany, G. Bell, and R. Rowland, “3-d motion system (”data-gloves”): application for parkinson’s disease,” IEEE Transactions on Instrumentation and Measurement, vol. 52, no. 3, pp. 662–674, 2003.
  223. M. Abtahi, S. B. Borgheai, R. Jafari, N. Constant, R. Diouf, Y. Shahriari, and K. Mankodiya, “Merging fnirs-eeg brain monitoring and body motion capture to distinguish parkinsons disease,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 28, no. 6, pp. 1246–1253, 2020.
  224. B. Anbalagan, S. Karnam Anantha, and R. Kalpana, “Novel approach to prognosis parkinson’s disease with wireless technology using resting tremors,” Wireless Personal Communications, vol. 125, no. 4, pp. 2985–2999, 2022.
  225. S. Sadhu, D. Solanki, N. Constant, V. Ravichandran, G. Cay, M. J. Saikia, U. Akbar, and K. Mankodiya, “Towards a telehealth infrastructure supported by machine learning on edge/fog for parkinson’s movement screening,” Smart Health, vol. 26, p. 100351, 2022.
  226. J. Kim, G. Lee, H. Jo, W. Park, Y. S. Jin, H. D. Kim, and J. Kim, “A wearable soft robot for stroke patients’ finger occupational therapy and quantitative measures on the joint paralysis,” International Journal of Precision Engineering and Manufacturing, vol. 21, no. 12, pp. 2419–2426, 2020.
  227. S. Sabry, M. Sahib, and T. Nayl, “Toward hand functions rehabilitation using the virtual world for pre-school children with cerebral palsy,” International Journal of Emerging Technologies in Learning (iJET), vol. 15, no. 9, pp. 110–122, 2020.
  228. C. Schuster-Amft, A. Henneke, B. Hartog-Keisker, L. Holper, E. Siekierka, E. Chevrier, P. Pyk, S. Kollias, D. Kiper, and K. Eng, “Intensive virtual reality-based training for upper limb motor function in chronic stroke: A feasibility study using a single case experimental design and fmri,” Disability and rehabilitation. Assistive technology, vol. 10, no. 5, pp. 385–392, 2014.
  229. R. Tavares, P. Abreu, and M. R. Quintas, “Data acquisition glove for hand movement impairment rehabilitation.” Int. J. Online Eng., vol. 12, no. 4, pp. 52–54, 2016.
  230. B.-S. Lin, I.-J. Lee, S.-Y. Yang, Y.-C. Lo, J. Lee, and J.-L. Chen, “Design of an inertial-sensor-based data glove for hand function evaluation,” Sensors, vol. 18, no. 5, p. 1545, 2018.
  231. F. Fei, S. Xian, X. Xie, C. Wu, D. Yang, K. Yin, and G. Zhang, “Development of a wearable glove system with multiple sensors for hand kinematics assessment,” Micromachines, vol. 12, no. 4, p. 362, 2021.
  232. J. Liu, Y. Luo, and Z. Ju, “An interactive astronaut-robot system with gesture control,” Computational intelligence and neuroscience, vol. 2016, 2016.
  233. C. K. Mummadi, F. P. P. Leo, K. D. Verma, S. Kasireddy, P. M. Scholl, J. Kempfle, and K. V. Laerhoven, “Real-time and embedded detection of hand gestures with an imu-based glove,” Informatics, vol. 5, no. 2, 2018.
  234. T. Kanokoda, Y. Kushitani, M. Shimada, and J.-i. Shirakashi, “Gesture prediction using wearable sensing systems with neural networks for temporal data analysis,” Sensors, vol. 19, no. 3, p. 710, 2019.
  235. R. Chauhan, B. Sebastian, and P. Ben-Tzvi, “Grasp prediction toward naturalistic exoskeleton glove control,” IEEE transactions on human-machine systems, vol. 50, no. 1, pp. 22–31, 2019.
  236. Y. Choi, S. Lee, M. Sung, J. Park, S. Kim, and Y. Choi, “Development of emg-fmg based prosthesis with pvdf-film vibrational feedback control,” IEEE Sensors Journal, vol. 21, no. 20, pp. 23 597–23 607, 2021.
  237. C. Yu, S. Fan, Y. Liu, and Y. Shu, “End-side gesture recognition method for uav control,” IEEE Sensors Journal, vol. 22, no. 24, pp. 24 526–24 540, 2022.
  238. E. Komi, J. Roberts, and S. Rothberg, “Measurement and analysis of grip force during a golf shot,” Proceedings of the Institution of Mechanical Engineers Part P Journal of Sports Engineering and Technology, vol. 222, pp. 23–35, 2008.
  239. H. Wang, M. Jeon, and Y.-G. Lee, “New design of a virtualglove for grasping applications,” Human Factors in Ergonomics & Manufacturing, vol. 20, no. 4, pp. 353–364, 2010.
  240. A. Boruah, N. M. Kakoty, T. Ali, and M. Malarvili, “Shape oriented object recognition on grasp using features from enclosure based exploratory procedure,” International Journal of Intelligent Robotics and Applications, pp. 1–17, 2022.
  241. L. Zhang, J. Pan, Z. Zhang, H. Wu, N. Yao, D. Cai, Y. Xu, J. Zhang, G. Sun, L. Wang et al., “Ultrasensitive skin-like wearable optical sensors based on glass micro/nanofibers,” Opto-Electronic Advances, vol. 3, no. 3, pp. 190 022–1, 2020.
  242. C. C. Vu and J. Kim, “Highly elastic capacitive pressure sensor based on smart textiles for full-range human motion monitoring,” Sensors and Actuators A: Physical, vol. 314, p. 112029, 2020.
  243. D. T. Rand and A. C. Nicol, “An instrumented glove for monitoring mcp joint motion,” Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, vol. 207, no. 4, pp. 207–210, 1993.
  244. L. Maréchal, C. Barthod, and J.-C. Jeulin, “First characterization of the expiratory flow increase technique: method development and results analysis,” Physiological measurement, vol. 30, no. 12, p. 1445, 2009.
  245. Y. Yao, S. Rakheja, and P. Marcotte, “Relationship among hand forces imparted on a viscoelastic hand-handle interface,” Measurement, vol. 145, pp. 525–534, 2019.
  246. J.-Y. Lee, J.-W. Choi, and H. Kim, “Determination of hand surface area by sex and body shape using alginate,” Journal of Physiological Anthropology, vol. 26, no. 4, pp. 475–483, 2007.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets